Results 21 to 30 of about 2,611,781 (277)

Deep learning for graphs

ESANN 2021 proceedings, 2021
Deep learning for graphs encompasses all those neural models endowed with multiple layers of computation operating on data represented as graphs. The most common building blocks of these models are graph encoding layers, which compute a vector embedding for each node in a graph using message-passing operators.
Bacciu, Davide, Bianchi, Filippo Maria, Paassen, Benjamin, Alippi, CesareC   +3 more
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A Deep Dive into Understanding Tumor Foci Classification using Multiparametric MRI Based on Convolutional Neural Network

, 2020
Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays. However, data scarcity has been the roadblock of applying deep learning models directly on prostate multiparametric MRI (
Carver, Eric, Chetty, Indrin, Elshaikh, Mohamed, Feldman, Aharon, Hearshen, David, Janic, Branislava, Lee, Joon, Liu, Chang, Movsas, Benjamin, Pantelic, Milan, Wen, Ning, Zong, Weiwei   +11 more
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Deep learning

American Journal of Orthodontics and Dentofacial Orthopedics
Flemish Government under the "Onder-zoeksprogramma ...
Axel-Jan Rousseau, Melvin Geubbelmans, Tomasz Burzykowski, Dirk Valkenborg   +3 more
  +7 more sources

Deep learning approach to scalable imaging through scattering media [PDF]

, 2019
We propose a deep learning technique to exploit “deep speckle correlations”. Our work paves the way to a highly scalable deep learning approach for imaging through scattering media.Published ...
Li, Yunzhe, Tian, Lei, Xue, Yujia
core   +1 more source

Deep learning in remote sensing: a review [PDF]

, 2017
Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields ...
Fraundorfer, Friedrich, Mou, Lichao, Tuia, Devis, Xia, Gui-Song, Xu, Feng, Zhang, Liangpei, Zhu, Xiao Xiang   +6 more
core   +4 more sources

Deep learning in the fog [PDF]

International Journal of Distributed Sensor Networks, 2019
In the era of a ubiquitous Internet of Things and fast artificial intelligence advance, especially thanks to deep learning networks and hardware acceleration, we face rapid growth of highly decentralized and intelligent solutions that offer functionality of data processing closer to the end user.
Andrzej Sobecki, Julian Szymański, David Gil, Higinio Mora   +3 more
openaire   +4 more sources

Performance Evaluation of Deep Learning Tools in Docker Containers

, 2017
With the success of deep learning techniques in a broad range of application domains, many deep learning software frameworks have been developed and are being updated frequently to adapt to new hardware features and software libraries, which bring a big ...
Chu, Xiaowen, Shi, Shaohuai, Xu, Pengfei
core   +1 more source

Deep learning

SIGGRAPH Asia 2019 Courses, 2018
Concepts, terminology, structures, no math, no code. Free open-source libraries do the hard work. My background: consultant, writer, director, etc.
Polson, Nicholas G., Sokolov, Vadim O.
  +7 more sources

On "Deep Learning" Misconduct

Proceedings of the 3rd International Symposium on Automation, Information and Computing, 2022
Accepted by ISAIC 2022, 8 pages, three figures.
openaire   +2 more sources

Deep learning? What deep learning?

South African Journal of Higher Education, 2003
In teaching generally over the past twenty years, there has been a move towards teaching methods that encourage deep, rather than surface approaches to learning. The reason for this being that students, who adopt a deep approach to learning are considered to have learning outcomes of a better quality and desirability than those who adopt a surface ...
openaire   +3 more sources